Optofluidics based lab-on-chip device for in situ measurement of mean droplet size and droplet size distribution of an emulsion

Figures 1(a) and (b), respectively, show the schematic and image of the device used for predicting droplet size and frequency by measuring the forward scattered signal (FSC). The device was also used for measuring mean droplet size (${{d}_{\text{Mean}}}$) and DSD of emulsions. The device is equipped with grooves to insert optical fibers. As shown, an optical fiber in Groove 1 was embedded to generate a focused illumination beam (of size ~10 µm). In order to avoid saturation of the photodiode due to direct exposure of the incident laser beam, the collection fiber in Groove 2 was placed $5{}^\circ$ anticlockwise with respect to the incident beam, and was used to obtain FSC. This design constraint is also consistent with the literature [43], which reports collection of FSC at $1{}^\circ$–$10{}^\circ$ with respect to the incident beam axis [43, 44].The PDMS microchannel device was fabricated using a standard photolithography technique followed by a soft lithography process, a detailed description of which is provided in our previous work [45]. The device includes an on-chip droplet generator to generate droplets of deionized (DI) water as discrete phase in oil as continuous phase. In our experiments with emulsions, the continuous W/O emulsion was introduced through the discrete phase inlet and oil was used to space out the droplets. The depth and width of the fiber channels were kept as $130~\mu \text{m}$, so that the standard available $10/125~\,\mu \text{m}$ single mode and $62.5/125\,~\mu \text{m}$ multimode fibers can be inserted into the grooves. The fiber grooves were filled with index matching liquid (Fiber Instruments Sales Inc., USA) to minimize reflection losses. The width of the main fluid channel was kept as 100 µm and the width of the flow focusing junction was kept as 30 µm. The depth of the fluid channel is kept as 130 µm throughout. The droplets self-migrate towards the center of the channel [43] due to deformability induced lift force acting on them.

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Mineral oil (Sigma Aldrich, Bangalore, India) was filtered using a $0.2\,\mu \text{m}$ polytetrafluoroethylene (PTFE) filter (Axiva Sichem Biotech, Chennai, India) to avoid the device clogging. The kinematic viscosity of the mineral oil was $15.6\times {{10}^{-6}}\,{{\text{m}}^{2}}\,{{\text{s}}^{-1}}$. To increase the stability of droplets, 5% wt/wt of surfactant Span 85 (Sigma Aldrich Bangalore, India) was added to mineral oil. DI water was filtered separately using a nylon $0.2\,\mu \text{m}$ filter (Axiva Sichem Biotech, Chennai, India) and $0.5 \%$ wt/wt of surfactant Tween 80 was added to prevent the sedimentation of water droplets in oil. Water was used as the discrete phase while oil was used as the continuous phase. The mean droplet size ${{d}_{\text{Mean}}}$ of an emulsion is directly proportional to the concentration of the discrete phase in the continuous phase [4]. W/O emulsions of different mean droplet size ${{d}_{\text{Mean}}}$ were obtained by varying the concentration (% volume) of dispersed phase (water) in the continuous phase (oil). DI water of volumes $25\,\mu \text{l}$, $50\,\mu \text{l}$, $75\,\mu \text{l}$, and $100\,\mu \text{l}$ were separately mixed with fixed volume ($\left. 2\,\text{ml}\right)$ of oil using a vortex mixer for 1.0 s, resulting in $1.25 \%$, $2.5 \%$, $3.75 \%$, and $5 \%$ of W/O emulsions.

Sensing of droplets and measurement of DSD in microchannels have been achieved using various optical interrogation techniques, for instance forward scattering [42], surface enhanced raman scattering [46], fluorescence [40, 41], and optical spectroscopy [47]. However, expensive, high power, potentially hazardous laser sources in the visible range (400 nm–650 nm) were used in these works. In contrast, in the present work, a cost effective, low power (1.0 mW), and non-hazardous [48] laser source near infrared range (1550 nm) was used for the sample illumination. In the previous works, a range of complicated and expensive optical detection systems including microscope coupled spectrometer [46], photon multiplier tubes (PMT) [40], and avalanche photodiode (APD) [42] were used for the measurements. In contrast, in the present work, we have used portable and cost-effective InGaAs photodiode for the detection.

Figures 2(a) and (b), respectively, show a block diagram and image of the in-house built optical detection system used for the measurements. A single mode ($10/125\,\mu \text{m}$) fiber coupled $~1.0~\text{mW}$, $1550~\text{nm}$ laser diode was used for illumination. The forward scatter (FSC) data was collected with multimode ($62.5/125\,\mu \text{m}$) fiber coupled InGaAs photodiode (Epitaxx Inc, Princeton, NJ). The scattered signal was amplified with a trans-impedance amplifier (TL084, Texas Instruments Inc., USA) before converting to digital signal using a microcontroller (Arduino Uno R3, Arduino, Italy). The data was serially transmitted to the terminal with the baud rate of 2.5  ×  10⁵ bits per second (bps). Data was analyzed on a personal computer (PC) using a Python code to obtain the residence time and scattered signal amplitude of each particle crossing the detection region (i.e. focused beam spot). A voltage regulator circuit was employed to obtain regulated voltage of $\pm 5$ V to supply power to laser source, trans-impedance amplifier, and microcontroller circuits.

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Syringe pumps (Kent Scientific Corporation, USA) were used to infuse the fluids into the microchannels. An inverted microscope (Axiovert A1, Carl Ziess GmbH, Gemany) coupled with high speed camera (SA3, Phtoron, USA) was used to capture the videos at 3000 frames per second to accurately capture the droplets. Images and videos were analyzed using ImageJ (Rasband, W. S., Image J, USA) to obtain the droplet generation rate (${{f}_{\text{d}}}$), and droplet diameter (${{d}_{\text{d}}}$). Mean droplet size (${{d}_{\text{mean}}}$) and DSD of the droplets were quantified with improved Naeubaer Haemocytometer (Marienfeld, Germany). Emulsion of volume $10\,\mu \text{l}$ was spread on a haemocytometer and images were captured using the inverted microscope. MATLAB (The Math Works Inc., USA) was used to process the images to obtain ${{d}_{\text{mean}}}$ and DSD.

Continuous stream of droplets was obtained by infusing water (discrete phase) and oil (continuous phase) into the microchannel at flow rates $q$ and $Q$ respectively. The percentage volume fraction of water ${{v}_{\text{W}}}$, defined as ${{v}_{\text{w}}}=\frac{q}{q+Q}\times 100$, was varied between 3 to 12%. The size of the generated droplets ${{d}_{\text{D}}}$ was controlled by changing the continuous to discrete phase flow rate ratio $f$, maintaining fluid flow velocity in the range of 3.5 mm s⁻¹ to 5.0 mm s⁻¹ in the microchannel. This corresponds to the variation of Reynolds number ($\text{Re}=U{{D}_{\text{h}}}/\nu$) in the range of 0.022–0.032, where $U$ is the flow velocity, ${{D}_{\text{h}}}$ is the hydraulic diameter of the channel and $\nu$ is the kinematic viscosity. At such low Reynolds number ($0.022\leqslant \text{Re}<0.032$), due to the deformability induced lift forces [43] and symmetric flow conditions in the microchannel, the droplets tend to move to the region with zero shear rate and thus self-align themselves at the centerline of the microchannel. Thus droplets are self-focused in both the horizontal and vertical directions. The stream of droplets was interrogated with a beam of laser light (1550 nm, 1.0 mW) and detected with the in-house built optical detection system explained in section 2.3. When a droplet traverses the detection region, it obstructs the passage of the laser beam, which results in a pulse in the detected signal as observed in figure 3. An in-house software was built using Python to serially receive the data at $2.5\times {{10}^{5}}$ bps. The data was passed through a moving average filter to remove the high frequency and random noise. The software program senses a droplet in the microchannel by detecting the presence of a pulse in the data. The width of the pulse corresponds to the time required by the droplet to cross the detection zone (residence time $\tau$), whereas the amplitude of the detected pulse denotes the scattered signal amplitude (${{v}_{\text{FSC}}}$). Figures 3(a)–(c) show the experimental images of the droplets and data captured using the optical detection system at continuous to discrete phase flow rate ratio (i.e. $f=Q/q$) $f=7$, $f=20$, and $f=25$, respectively. It can be observed from figure 3 that as flow rate ratio ($f$) increases, the size of the droplets ${{d}_{\text{D}}}$ decreases and correspondingly the pulse signal reduces in amplitude (${{v}_{\text{FSC}}}$) and duration (${{\tau}_{\text{D}}}$).

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For bigger droplets ($62~\,\mu \text{m}$ in figure 3(a)), the advancing and receding edges of a droplet resulted in two peaks within a single pulse, as shown in figure 3(a). This observation is consistent with the work reported previously on the detection of droplets of comparable sizes [42]. However, as the droplet becomes smaller, only one peak was observed in a pulse as shown in figures 3(b) and (c), this is attributed to the fact that as the size of the droplet reduces, curvature of the droplet increases significantly and peaks approach each other, finally resulting in a single peak for a given droplet.

Images captured from optical microscopy were analysed using ImageJ to determine exact size of the droplets ${{d}_{\text{D}}}$. Figure 4(a) shows the mean droplet size at various flow rate ratios $f$. It is observed that as the flow rate ratio $f$ increases, the size of droplets ${{d}_{\text{D}}}$ decreases. The error bar in the plot shows the standard deviation of the data. Figure 4(b) represents the scatter plot of FSC ${{v}_{\text{FSC}}}$ versus residence time ${{\tau}_{\text{D}}}$ of the droplets at various flow rate ratios $f$. The results clearly show that as the flow rate ratio $f$ increases from $7$ to $25$ i.e. as the size of droplets decreases, the strength of both the scattered signal ${{v}_{\text{FSC}}}$ and residence time ${{\tau}_{\text{D}}}$ decreases. Consequently, we observe distinct colonies on the scatter plot depending on the flow rate ratio or the resulting droplet size. The different colonies observed on the plot also indicate that the droplets align themselves at the center of the channel and pass through the detection region single-file. For a given flow rate ratio $f$ or the corresponding droplet size ${{d}_{\text{D}}}$, the small variation in residence time indicates that the droplets are focused and thus travel at the same velocity. Similarly, the small variation in the forward scatter signal ${{v}_{\text{FSC}}}$ indicates that the droplets travel single-file. From figure 4(b), it is observed that the spread in the scattered signal ${{v}_{\text{FSC}}}$ and residence time ${{\tau}_{\text{D}}}$ is greater at higher flow rate ratios $f$ (i.e. for smaller droplets). This can be attributed to the fact that due to lower migration velocity [43], the smaller droplets may not get focused at the center of the channel and may be present at different locations from the center. As a result, a larger variation in the FSC signal ${{v}_{\text{FSC}}}$ and residence time ${{\tau}_{D}}$ is oberserved in the case of smaller droplets in comparison to bigger droplets (which get focused at the center).

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The previous literature [49] extensively reports the formulation and sensing of droplets of a size comparable to that of the microfluidic channels (such droplets are also called plugs). In the present work, a much lower volume fraction of water ${{v}_{\text{W}}}$  =  3% was used to generate and detect monodispersed droplets of size as small as $15\,\mu \text{m}$, in contrast to the higher ${{v}_{\text{W}}}$ in the range 10%–60% used in the works reported in the literature [40, 42, 46, 47]. The size of the plugs generated and detected previously vary from $100\,\mu \text{m}$ to $300\,\mu \text{m} ~$ [41]. Furthermore, we reported the scatter plot of forward scatter signal ${{v}_{\text{FSC}}}$ versus the residence time ${{\tau}_{\text{D}}}$ for droplets of different sizes and provide some insight on the large variations in the signal in case of smaller droplets.

The device was also employed for the measurement of droplet generation rate (${{f}_{\text{D}}}$). The time gap between two consecutive droplets ${{t}_{\text{D}}}$ to cross the detection region was measured and the droplet generation rate ${{f}_{\text{D}}}$ was calculated as the reciprocal of ${{t}_{\text{D}}}$. The videos captured using the microscope were analysed to determine the droplet generation rate ${{f}_{\text{D}}}$ and compared with that obtained from the optical detection system as shown in figure 4(c). The droplet generation rate ${{f}_{\text{D}}}$ was determined for each pair of consecutive droplets and the mean of the data is taken. The error bar in figure 4(c) corresponds to the standard deviation of data. An excellent match (within a maximum error of 2%) between the data obtained from microscopy and the optofluidic device is observed. Thus the proposed device can be used as a droplet counter.

The variation in the mean of the scattered signal ${{v}_{\text{FSC}}}$ and the normalized residence time $\tau$ with droplet size ${{d}_{\text{D}}}$ is depicted in figures 5(a) and (b) respectively. In both the plots, error bars represent the standard deviation of the data. In figure 5(b), residence time of the droplet ${{\tau}_{\text{D}}}$ is normalized with time scale of the device i.e. $\tau =\frac{{{\tau}_{~\,\text{D}}}}{w/u}$, where $w$ is width of the microchannel and $u$ is the continuous phase velocity in the microchannel, since residence time depends on superficial velocity $u$ of the fluid and is proprtional to total flow rate ($q+Q$). Figure 5(a) shows that the the strength of the scattered signal ${{v}_{\text{FSC}}}$ increases linearly for smaller droplets but becomes nonlinear at higher droplet size. This characteristic can be attributed to the fact that the strength of scattered signal ${{v}_{\text{FSC}}}$ would saturate as droplet size approaches the size of microchannel ($100\,\mu \text{m}$ in the present device). At such droplet sizes, the droplets become deformed and the lateral dimension (normal to flow direction) of the droplet which contributes to the forward scatter does not change any further. From figure 5(b), it is observed that the value of residence time $\tau$ increases linearly with the size of the droplet. This is explained by the fact that the residence time depends on the droplet size along the flow direction, which increases proportionately with increase in the droplet size. Thus we find that the normalized residence time $\tau$, which exhibits linear charcteristics, is a better parameter to characterize the size of droplets. However, other properties including refractive index and granularity can be charcterized using scattered signals [44]. Here, the scattered signal ${{v}_{\text{FSC}}}$ was fitted to a quadratic polynomial fitted with ${{R}^{2}}$ value of 0.983, wheras residence time $\tau$ was fitted to a linear curve with the ${{R}^{2}}$ value of 0.998. The droplet size was correlated with the residence time as follows: ${{d}_{\text{D}}}=A\tau$, where the value of $A$ is found to be $142$. This correlation was further used in the prediction of droplet size in case of emulsions.

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In the literature, the residence time ${{\tau}_{\text{D}}}$ of droplets or the pulse width of signal was used to represent the size of droplets ${{d}_{\text{D}}}$ [42]. However, the residence time ${{\tau}_{\text{D}}}$ could vary with the variation in the flow rate in a microchannel ($q+Q$), thus it is a very intricate process of measurement to gain the absolute size of droplets unless the flow velocity is kept fixed [46]. Here, we normalize the residence time ${{\tau}_{\text{D}}}$ with superficial velocity of the fluid and correlate the normalized residence time $\tau$ with droplet size ${{d}_{\text{D}}}$ to eliminate the effect of fluid velocity on the measurement. Similarly, a correlation between amplitude of the forward scatter signal ${{v}_{\text{FSC}}}$ with droplet size ${{d}_{\text{D}}}$ was studied [41]. However, we conclude that the normalized residence time $\tau$ is a better parameter for predicting ${{d}_{\text{D}}}$ due to its linear correlation with ${{d}_{\text{D}}}$ whereas ${{v}_{\text{FSC}}}$ correlates nonlinearly with ${{d}_{\text{D}}}$.

Experiments were performed using emulsions with varying volume fraction of water ${{v}_{\text{W}}}$ between 1.25%–5%, formulated as explained in section 2.2. The volume fraction of water used in the emulsions is consistent with that used for the droplet generation experiments presented in section 3.1. Mineral oil was used as a sheath fluid to space out the droplets, to avoid a large number passing the detection region simultaneously. The emulsions were formulated as explained in section 2.2 and used as a sample for carrying out the measurements. When each droplet crosses the detection region, the forward scattered signal ${{v}_{\text{FSC}}}$ and normalized residence time $\tau$ of each event was collected. From the measured normalized residence time $\tau$, the droplet size ${{d}_{\text{D}}}$ of each event was predicted using the correlation presented in section 3.2. The predicted droplet size was further compared with the results obtained using microscopy. Using the optofluidic device, the mean droplet size ${{d}_{\text{Mean}}}$ of each emulsion was determined from a large number of events crossing the detection region. Emulsion of volume $10~\mu \text{l}$ was dispensed on a haemocytometer and images were captured using optical microscopy at 20x  magnification. A MATLAB image processing toolbox was used to calculate the mean droplet size ${{d}_{\text{Mean}}}$ of the emulsion. Figure 6(a) presents ${{d}_{\text{Mean}}}$ of emulsions prepared with different percentage of dispersed phase. The data obtained from the optofluidic device and predicted by the correlation is in good agreement (within 10%) with that measured using microscopy. It is observed that, with all other parameters kept fixed, the mean droplet size ${{d}_{\text{Mean}}}$ increases with the increase in the concentration of the dispersed phase [4].

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The DSD of the emulsion was also measured using the optofluidic device. Figure 6(b) shows the size distribution of droplets in the emulsion with the dispersed phase of $3.75\%$ in terms of normalized droplet count $\gamma$ versus droplet size ${{d}_{\text{D}}}$. Here, for a particular droplet size, the count is normalized by the total count of droplets of all sizes. Data sets obtained from optical microscopy and the optofluidic device for each emulsion were separately fitted to normal distributions, as observed from figure 6(b), with ${{R}^{2}}$ 0.88 and 0.99. The standard deviation $\sigma$ of each normal distribution was considered to represent DSD (polydispersity) of emulsion. The standard deviations of the normal distribution curves with the data obtained from microscopy and optofluidic device are in good agreement (within 13%).

The detection and measurement of monodispersed droplets generated inside microchannels have been reported [41, 42]. However, the measurement of mean droplet size ${{d}_{\text{Mean}}}$ and DSD of a polydispersed emulsion in a microchannel have not received much attention. Here, we report the measurement of these characteristics of a polydispersed emulsions in a microchannel. Recently, measurement of the ${{d}_{\text{Mean}}}$ and DSD of polydispersed microparticles using complicated and expensive macroscopic acoustic attenuation spectroscopy (AAS) setup and its comparison with conventional microscopy setup was reported [23]. The comparisons showed 20% and 44% difference in ${{d}_{\text{Mean}}}$ and DSD respectively between the AAS and microscopy data. In the present work, the difference between our optofluidics and microscopy data was found to be only 10% and 13% for ${{d}_{\text{Mean}}}$ and DSD, respectively, which indicates the accuracy of our optofluidic system. This improvement can be attributed to the self-aligning characteristics of the droplets (alignment at the center due to lift forces) in the microchannels which minimizes the uncertainty in the position of the droplets and improves the consistency in the signal as explained in section 3.1.